MEASURING THE EFFECTS OF
MONETARY POLICY FOR TURKEY
Hakan Berument
Department of Economics
Bilkent University
06900 Ankara, Turkey
Phone: +90 312 290 2342
Fax: +90 312 266 5140
e-mail: berument @bilkent.edu.tr
URL: http:\\www.bilkent.edu.tr/~berument
N. Nergiz Dincer*
State Planning Organization
Economic Modeling Department
Necatibey cad. No:108 Room: 1016
06100 Yucetepe, Ankara, Turkey
Phone: + 90 312 294 6036
Fax: +90 312 294 6077
e-mail: ndincer@dpt.gov.tr
*
The views presented here are those of the authors; they do not necessarily reflect the official position of
the State Planning Organization, or its staff.
MEASURING THE EFFECTS OF
MONETARY POLICY FOR TURKEY
Abstract
This paper proposes a specification to identify the monetary policy for a small
open economy, Turkey. The monetary policy is measured by using the spread between
the Central Bank’s interbank interest rate and the depreciation rate of the domestic
currency. A VAR type of model is used to identify monetary policy covering the period
1986:05-2000:10. The results suggest that tight monetary policy has a transitory effect on
output but a permanent effect on prices. This specification is free of some puzzles such as
the price puzzle and the liquidity puzzle that hampered some of the previous studies.
JEL Codes: E43, F50 and E52.
Keywords: Monetary Policy, Turkey, Interest Rate Spreads and VAR.
1
1. Introduction
Understanding what monetary policy can do to enhance economic performance
(and, just as importantly, what it cannot do) is a continuing challenge for economic
policymakers around the world (Mankiw, 2003). For good policy-making, it is necessary
to measure how monetary policy is affected by different macroeconomic conditions and
how it affects the macroeconomy. The literature contains many studies about monetary
policy for both developed and developing countries but there are few empirical studies
that identify a specific monetary policy and its effects (Cushman and Zha, 1997). In this
paper, we use Vector Autoregressive (VAR) methodology to identify the monetary policy
in Turkey --a small open economy with high and persistent inflation.
Large structural economic models were used to assess monetary policy in the
studies such as Dornbusch and Giovannini (1990), Frankel (1987), and Edisson and
Tryon (1988). However, Sims (1980) and Cushman and Zha (1997) argued that many
identifying restrictions employed in such models were not credible; suggested using
impulse responses from reduced-form vector autoregressions (VARs) for policy analyses
with better predictive power. Studies using this methodology included Bernanke and
Blinder (1992), Strongin (1995), and Christiano, Eichenbaum, and Evans (1994), among
others.
Monetary aggregates such as the monetary base, M1 and M2 have traditionally
been used to measure the stance of monetary policy; however, it is generally agreed that
the growth rates of monetary aggregates vary according to a variety of non-policy
influences. An alternative measurement of monetary policy is interest rates. But neither
monetary aggregates nor interest rates were effective in identifying monetary policy. And
most of the studies were focused on closed economies such as the US economy. For a
developing economy like Turkey, which is a small open economy, there are other factors
affecting the identification of monetary policy; exchange rate appears to be the first
candidate.
2
In this study, spread is used as the measure of monetary stance; this is the extent
to which interbank interest rates exceed the depreciation rate of the local currency. The
central bank may cut the liquidity provided to the public by raising interest rates at a
given level of depreciation or sell foreign currency at a given level of interest rate. If the
central bank pegs the interest rate at or below the depreciation rate, the public may prefer
to hold foreign currency rather than domestic currency. Thus, keeping the domestic
interest rate above the perceived depreciation rate is the first requirement of tight
monetary policy; the higher spread means tighter monetary policy. We do not suggest
that the central bank control both of these instruments at the same time; but while
controlling one, the CB could watch the other. The advantage of using spread to measure
monetary policy is that it can still be a monetary policy measure regardless of whether the
central bank uses exchange rate targeting or interest rate targeting.
This study uses monthly data for Turkey and covers the period 1986:05-2000:10.
We end the study in 2000:10 because Turkey experienced a severe crisis beginning in
November 2000 with resulting changes in structure. Turkey switched to an exchange rate
regime that led to an increase in exchange rate volatility with its perceived risk. However
the period we study has particular importance for Turkey. This is the period after the mid
1980s when outward oriented policies were implemented, hence this period is the post
liberalization era. Turkey is also a unique laboratory for analyzing the development of
inflation; there was high and persistent inflation from the mid 1970s without becoming
hyper-inflation. Second, during this period, the central bank targeted both exchange rates
and interest rates. Overall, these characteristics of the economy of Turkey will allow us to
grasp the effects of monetary policy and the economic outcomes of such policy.
Most of the previous studies identifying monetary policy look at developed
countries rather than developing economies, like Turkey. Moreover, most of the earlier
studies proposed criteria to identify the monetary policy; however, for most of them, the
price puzzle, the liquidity puzzle and the exchange rate puzzle could not be eliminated.
Three abnormalities (puzzles) can be describe as follows: when a tight monetary policy
is identified with positive interest rate innovations, it seems that prices increase rather
than decrease – price puzzle; an innovation in monetary aggregates is associated with
rising (rather than decreasing) interest rates – liquidity puzzle; a positive innovation in
3
interest rates is associated with the impact of the depreciation of the local currency rather
than its appreciation – exchange rate puzzle. Berument (2007) is an exception in that he
proposes a specification free of puzzles to identify Turkey’s monetary policy; he uses a
VAR model composed of 6 variables: output, prices, commodity prices, exchange rate,
spread and money. However his specification there has a couple of drawbacks. First,
using 6 variables leads to over parameterized models. Second, the high correlation
between prices and commodity prices, and spread and the exchange rate would lead to
misleading results when used in the same specification. Third, Berument’s results (2007)
suggest that although prices decrease permanently when the output measure is industrial
production, the decrease is temporary when other output definitions are used. The same
type of differences in the response of commodity prices and exchange rates appear
between the specification and different output measures. This calls into question the
robustness of the specification. Our study identifies Turkey’s monetary policy by
proposing a specification which is free of puzzles, and which overcomes the problems of
Berument’s specification (2007).
The outline of the paper is as follows: Section 2 presents the developments in
monetary policy in Turkey. Section 3 discusses the model. Section 4 explains the data set.
Results are discussed in Section 5. Finally, Section 6 contains conclusions.
2. Developments in Monetary Policy in Turkey
The 1980s in Turkey were years of liberalization and an economic strategy that
was oriented outward. There were several stages of economic policy leading up to the
capital account liberalization of 1989: determining exchange rates by the Central Bank,
setting interest rates free, permitting residents to hold foreign exchange deposits,
establishing the interbank money market and Istanbul Stock Exchange and starting to use
indirect monetary policy instruments in policy implementation. Finally, in 1990 Turkey
adopted convertibility of the Turkish lira.
Since the Central Bank was not independent during the 1980s and 1990s, the
political cycles were a dominant force in determining the stance of monetary policy (see
the discussions in Sayan and Berument, 1997; Ergun, 2000; Berument and Neyapti, 1999
and Berument, 2007). The 1986-1990 period was characterized by unstable political
4
structure and frequent elections--mid-term elections in September 1986, municipal
elections in June 1987, a referendum in September 1987, elections in November 1987
and another referendum in September 1988. Figure 1, which reports the cumulative sum
of spread innovations shows that the stance of monetary policy followed a volatile path
between tight and loose monetary policy during this period; this path parallels our
identification scheme. In particular, Figure 1 reports the cumulative sum of spread
innovations using a 4 variable VAR model, where the ordering is output, prices, spread
and money and the lag order is 1. The model is identified using the Choleski
Decomposition; therefore, the third equation of the VAR, which models the spread, is
explained by the lags of the four variables and the current levels of output and price. The
increases and decreases represent monetary tightening and easing.
[Insert Figure 1 Here.]
1990 was the year that the central bank first announced a monetary program with
a medium term horizon. The monetary program aimed at controlling the credit extended
to the public sector, thus the domestic assets. This resulted in tight monetary policy for
the year and the balance sheet targets of the monetary program were almost realized.
Because of the uncertainties of the Gulf War, a monetary program was not announced in
1991. The increased need for public sector borrowing resulted in higher inflation despite
a contraction in the economy. Monetary easing was seen within the year, because of both
the Gulf War and the elections in November 1991.
The period before the elections in June 1992 was characterized by monetary
easing. Following this until April 1993, tight monetary policy was observed. For 19921993 the Central Bank of the Republic of Turkey (CBRT) announced a new monetary
program based on tight monetary policy, despite the lack of fiscal discipline. The
monetary program ended with an announcement by the Prime minister that she would
decrease interest rates to boost the economy. Loose monetary policy has persisted since
the crisis in 1994.
The financial crisis forced the government to sign a Stand-by Agreement with the
IMF. The Agreement heavily influenced the monetary policy of CBRT as the IMF urged
a ceiling on net domestic assets and a floor on net international reserves. Following these
5
policy measures, the economy recovered during 1995. Inflation declined to 65.6 per cent
in 1995 from 149.6 per cent in 1994. However, the election atmosphere forced CBRT to
follow an expansionary monetary policy.
During 1996-1997, the CBRT publicly announced that monetary policy
implementation would be based on achieving stability in financial markets rather than
decreasing the inflation rate. This period was characterized by the loose monetary policy.
Following the Asian crisis in mid-1997 and a decrease in Turkey’s ratings by
international agencies, CBRT tightened its monetary policy. The Central Bank
maintained this monetary stance until the third quarter of 1999, when the earthquake hit.
The CBRT had to follow a supportive monetary policy to help economic recovery.
At the end of 1999, a new stand-by agreement with IMF based on a fixed
exchange-rate regime was signed. The CBRT kept its loose monetary policy during this
program, as outlined in the literature. Agenor and Montiel (1999) suggest that monetary
policy is looser in an exchange rate-based stabilization program compared to a monetarybased stabilization program.
We provide a set of plots to demonstrate the relationship between the monetary
policy measure proposed here and the money aggregates. Figure 2 plots the M1 plus repo
(M1R) growth variables and Figure 3 plots the M2 plus repo (M2R) growth variables
along with the accumulated spread innovations. Figure 2 shows that the relationship
between M1R and the spread innovation is quite strong during the sample period except
for 1992 and the period between 1996 and 1999. Figure 3 suggests that the relationship
is weaker for M2R growth compared to M1R growth. This relationship is also weak for
1988. These two figures suggest that even if there is a relationship between money
growth measures and spread measure, these relationships are not stable.
[Insert Figure 2 here]
[Insert Figure 3 here]
6
3. Data and Descriptive Statistics
Our paper uses monthly data covering the period 1986:05 to 2000:10. We use the
following data terms: the basket is the combined TL value of 1 US Dollar plus 1.5
Deutsche Mark1. Spread, is the spread between the interbank interest rate and the
depreciation of the basket; pt is the wholesale price index is the price measure; mt is
M1+repo the money measure. The major reason for using repo transactions as our money
measure is their wide use as a saving instrument during the period of this study. Repo
transactions were preferred because of their liquidity; they were mainly overnight and
offered a high rate of return. There were three income measures, yt, is used at different
times: industrial production, the private sector capacity utilization rate and the number of
housing permits issued by local authorities2.
Our data are taken from the data delivery system of the CBRT
(http:\\tcmbf40.tcmb.gov.tr/cbt.html). All data were used in their logarithmic forms
except the capacity utilization rates and the spread (following Sims, Stock and Watson,
1990)3.
The variables used in the model are not seasonally adjusted, but the equations
include seasonal dummies to account for seasonal changes. The 1994 crisis period is
corrected with dummies --1 for March 1994, April 1994, and May 1994 and 0 at other
times. Repo figures that are included in the money measure were not available before
November, 1995; we used a dummy variable of 1 for that period.
4. Model Specification
The aim of this paper is to identify the effect of the monetary policy. We used a
Vector Autoregressive (VAR)model; this specification allowed us to separate the
unaffected components of the central bank’s policy from the state of the economy,
1
The choice of foreign currencies and their weights in the basket is determined by the CBRT. This is the
official basket the CBRT used during the period studied.
2
When the income measure is taken as the capacity utilization rate, the data set starts from 1991:02 and
when the income measure is taken as the number of housing permits given by local authorities, then the
data set starts from 1991:01.
3
One could argue for including the level variables in their first difference or in Vector Error Correction
form. Johansen λ - trace and λ - max tests reject its null of no cointegration for the system. Thus using
differenced series would lead to over differencing.
7
assuming that the central bank sets its policy considering both its goals and the state of
the economy. Following Christiano, Eichenbaum and Evans (1999) and Berument
(2007), we followed a VAR type of specification to achieve our goal.
The VAR specification that we use in this paper includes income, yt; the
logarithm of prices index, pt; the spread between the interbank interest rate and the
depreciation rate, spread; and the logarithm of money, mt. To identify the monetary
policy of the central bank, we wanted to isolate those components of the central bank that
are not affected by other variables. Therefore, the ordering of the VAR specification in
this study is as follows: (yt, pt, spreadt, mt); thus although income and prices do affect the
Central Bank’s policy reaction, the monetary policy setup does not have any concurrent
effect on income and prices. The lag length of the VAR specification is set as two
following the Hannan-Quinn and the Shwarz information criteria.
We also considered lag orders of the VAR system of 3, 6, 9 and 12 . The results
are robust when industrial production and housing are taken as the income measures. The
only abnormality observed was the price puzzle – positive innovation in spread increased
prices rather than decreasing them. This puzzle held for the VAR specifications with 6
and 9 lags and when capacity utilization was the income measure.
5. Empirical Results
The VAR models supported our argument, using Turkey as a case study.
5.1. Impulse Response Functions
The effects of tight monetary policy - positive innovation in spread - are assessed
by using impulse response function analyses; these are calculated by the Monte Carlo
method of 500 draws from the estimated asymptotic distribution of the VAR specification
and its covariance matrix (Masconi, 1998; Doan, 2000) using the routine called
MALCOLM. The specification is in the order of industrial production, prices, spread and
money. Figure 4 shows the results for 18 periods. . The middle line shows the point
estimates and the other two lines indicate the 95% confidence intervals.
In the first diagram, the effect on output of a positive innovation in spread is
transient; it decreases for two months in a statistically significant fashion and then regains
8
its initial level. In other words, tight monetary policy is associated with a drop in
industrial production and follows a hump-shaped pattern, which is consistent with
Berument (2007) and the Fuhrer and Moore model (Fuhrer and Moore, 1995a; and Fuhrer
and Moore, 1995b) as presented in Walsh (1998: 472-4). The intuition behind this is that
when monetary policy is exogenous, inflationary pressure, excess liquidity demand and
shocks from the rest of the world do not affect it. Therefore, output level and prices do
not increase, as our specification suggests.
Likewise, the second diagram suggests that price level decreases for three months
in a statistically significant fashion. There may be more than one theoretical reason for
this outcome, such as lack of persistence of the monetary policy of CBRT, and the
validity of uncovered interest rate parity for a given level of foreign interest rate.
The
third diagram in the figure shows the movement of spread when there is one standard
deviation innovation in the spread. The innovation in spread persists only for three
months and then disappears.
Finally, the fourth diagram shows that money decreases
with a positive innovation in spread. However, the effect of monetary tightening on
money is not statistically significant.
[Insert Figure 4 Here]
For robustness, we used alternative definitions of the variables in our
specification. In Figure 5 the capacity utilization rate of the private sector is used for
income and the results obtained from the impulse response analysis are consistent.
Finally, in Figure 6 the output definition is housing permits as a measure of income. The
results are similar to the results for the other two definitions. Therefore, our specification
is considered robust for various definitions of output.
[Insert Figure 5 here]
[Insert Figure 6 here]
Next, we used M2 plus Repo (M2R); this is a broader measure of money than M1
plus Repo. The results are consistent. To be precise, a positive innovation in spread
9
increases M2R rather than decreases it. Moreover, an increase in M2R decreases prices
rather than increasing them4.
In addition, we tried an alternative variable for measuring monetary policy; we
will discuss the effects of a positive innovation in the monetary aggregate. it is common
to identify the monetary policy by using money aggregate (see for example Barro, 1977
and Mishkin, 1983). The effect of a one standard deviation in m is presented in Figure 7
when industrial production is used as the output definition. Monetary tightening
introduces a statistically significant increase in output for 2 months as shown in the first
diagram of Figure 7.
Price increases and spread decreases can be followed from
diagrams 2 and 3. Figure 8 and 9 show the responses of the variables to a positive
innovation in m, with different definitions of output similar to our base case. The results
are similar to what we obtained when spread was used to identify monetary policy.
[Insert Figure 7 Here]
[Insert Figure 8 Here]
[Insert Figure 9 Here]
To further analyze robustness, we performed the analysis in a restricted period,
1994:08 to 2000:10. We did this because some studies suggest (Ozatay, 2000; Alper and
Onis, 2001; and Ertugrul and Selcuk, 2002) that the 1994 financial crisis in Turkey
changed the structure and behavior of financial markets. Our results suggest that when
monetary policy is identified by spread, output decreases with monetary tightening.
Conversely, prices decrease with a positive innovation in spread, but the decrease is not
statistically significant. The sub-period estimations are repeated with different output
definitions. Although the results are robust for two definitions of output, the outcome of
the housing variable is not robust. In other words, output increases in a statistically
significant fashion in the sub periods when the housing variable used to define output. In
conclusion, the sub-period impulse-response function analysis is robust for two
definitions of output--industrial production and capacity utilization rate--but monetary
tightening results in an increase in output when it is measured by the housing variable.
4
The results are not reported here, but available from the authours upon request.
10
The analyses are also repeated for different sub-periods--1986:05-1997:12 and
1998:01- 2000:10. The results are similar in the first sub period. However, for 1998:012000:10, the results are not consistent with the base case. This could perhaps be
explained by the shortness of the period.
Finally, we reordered the variables in the VAR setting, making spread the last
variable: the extreme information assumption –central bank knows income and prices
before setting up spread. The results suggest that our analysis is robust in re-ordering the
variables in the VAR setting..
5.2. Forecast Error Variance Decomposition
Figures 10-12 contain forecast error variance decompositions that analyze the
contribution of monetary policy volatility to the volatility of output, prices and money.
Figure 10 reports the percentage of the variance of industrial production, prices, spread
and money that are explained by the variability of spread for the first 18-day ahead
forecast error variance decompositions. Figures 11 and 12 repeat the same analyses for
the other two definitions of income.
[Insert Figure 10 Here.]
[Insert Figure 11 Here.]
[Insert Figure 12 Here.]
These three figures suggest that the variability of spread in output and prices is not
statistically significant; monetary policy variability does not explain the variance of
output and prices. This is unexpected but parallels the findings of Kim (1999) and Kim
and Roubini (2000). On the other hand, the most of the variation of spread is also
explained by itself. This result supports our claim that spread is exogenous and not
explained by either income or prices.
5.3. Discussion
This paper aims to identify the monetary policy using the VAR methodology. In
the literature, the identification of monetary policy using VAR has been associated with
11
the liquidity puzzle and the price puzzle. The liquidity puzzle is the theoretically
unexpected positive relationship between money aggregates and interest rates (Leeper
and Gordon, 1992); the price puzzle is the price increase that is seen when interest rates
are used as the monetary policy measure. Many studies have tried to solve these puzzles
by using different identification schemes and variables. We used spread as the monetary
policy measure, eliminating the price puzzle5.
Moreover, the empirical evidence
provided here on the effect of spread innovations on prices and income are stronger than
evidence provided by Berument (2007).
6. Conclusion
Identifying monetary policy is necessary for good policy-making but it is not
easy. Most of the research has focused on the US and other developed countries. This
paper proposes a specification to identify monetary policy for a small open economy−
Turkey. A similar specification could be used for other developing countries.
The spread between the Central Bank’s interbank interest rate and the
depreciation rate of the domestic currency is taken as the measurement of monetary
policy. The results suggest that tight monetary policy has a transient effect on output,
causing output to decline for three months in a statistically significant fashion; the
decrease in prices is also temporary. Thus it is not advisable to use monetray policy to
stimulate the economy coniststently but monetray policy could be a tool to hamper the
volatility of business cycles. The results are consistent with previous studies (Sims,
1992; Eichenbaum and Evans, 1995; Grilli and Roubini, 1995; Kim and Roubini, 2000;
Berument, 2007). The puzzles such as price puzzle and the liquidity puzzles that some of
the previous studies faced are eliminated. This further supports the validity of our
specification.
5
For further discussion on the puzzles and their elimination see Berument (2007).
12
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Figure 1: Implied Stance of Monetary Policy: Accumulated Summation of Spread
Innovations
15
Figure 2: Stance of Monetary Policy and M1R Growth
16
Figure 3: Stance of Monetary Policy and M2R Growth
17
Figure 4. Effects of Spread when industrial production is used as the definition of output
i.
Response of IP to Spread
ii. Response of P to Spread
iii.Response of spread to spread
iv.Response of M to spread
18
Figure 5. Effects of Spread when capacity utilization is used as the definition of output
i.Response of capacity utilization to Spread
ii. Response of P to Spread
iii.Response of spread to spread
iv.Response of M to spread
19
Figure 6. Effects of Spread when housing is used as the definition of output
i.Response of housing to Spread
ii. Response of P to Spread
iii.Response of spread to spread
iv.Response of M to spread
20
Figure 7. Effects of Money when industrial Production is used as the definition of output
i.Response of IP to M
ii. Response of P to M
iii.Response of spread to M
iv.Response of M to M
21
Figure 8. Effects of Money when capacity utilization is used as the definition of output
i.Response of capacity utilization to M
ii. Response of P to M
iii.Response of spread to M
iv.Response of m to M
22
Figure 9. Effects of Money when Housing is used as the definition of output
i.Response of housing to M
ii. Response of P to M
iii.Response of spread to M
iv.Response of m to M
23
Figure 10. Forecast Error Variance Decomposition of Spread when industrial production
is used as the definition of output
i.FEV of IP due to Spread
ii. FEV of P due to Spread
iii. FEV of spread due to spread
iv. FEV of M due to spread
24
Figure 11. Forecast Error Variance Decomposition of Spread when capacity utilization is
used as the definition of output
i.FEV of capacity utilization due to Spread
ii. FEV of P due to Spread
iii. FEV of spread due to spread
iv. FEV of m due to spread
25
Figure 12. Forecast Error Variance Decomposition of Spread when housing is used as the
definition of output
i.FEV of housing due to Spread
ii. FEV of P due to Spread
iii.FEV of spread due to spread
iv.FEV of M due to spread
26